Models for Counts and Particle Size Distributions of Sub-visible Particle Data

Jorge Quiroz; Elsa M. Vazquez; Jeffrey Wilson; Anita Dabbara; Jason Cheung

doi:10.5731/pdajpst.2020.011510

Abstract

Traditional statistical analyses of sub-visible particle data are usually based on either descriptive statistics, normal-based methods, or standard Poisson models. These methods often do not adequately describe the counts or particle size distribution, or ignore relevant information represented in the data, such as count correlation. Therefore, any meaningful analyses of sub-visible particle data require a reasonable representation of counts and the particle size distribution as well as a means of addressing the correlation in the data. Such comprehensive approaches are not widely available or known when analyzing sub-visible particle data. In this paper, we propose the use of generalized linear mixed models to analyze counts and the particle size distribution of sub-visible particle data. These models make optimal use of the information in the data, and allow flexible approaches for the analyses of a wide range of data structures. They are readily accessible to practitioners through the use of modern statistical software. These models are demonstrated with two numerical examples using two different data structures.

Received March 11, 2020.
Accepted October 23, 2020.

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